Visium
Mouse lung
GSM5808057
bronze Interactive viewer H&E lung adenocarcinoma Provisional
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM5808057_KP_4_hires_image': DataArray[cyx] (3, 1998, 2000)
│ └── 'GSM5808057_KP_4_lowres_image': DataArray[cyx] (3, 600, 600)
├── Shapes
│ └── 'GSM5808057_KP_4': GeoDataFrame shape: (2546, 2) (2D shapes)
└── Tables
└── 'table': AnnData (2546, 32289)
with coordinate systems:
▸ 'GSM5808057_KP_4', with elements:
GSM5808057_KP_4_hires_image (Images), GSM5808057_KP_4_lowres_image (Images), GSM5808057_KP_4 (Shapes)
▸ 'GSM5808057_KP_4_downscaled_hires', with elements:
GSM5808057_KP_4_hires_image (Images), GSM5808057_KP_4 (Shapes)
▸ 'GSM5808057_KP_4_downscaled_lowres', with elements:
GSM5808057_KP_4_lowres_image (Images), GSM5808057_KP_4 (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("jh4NRTq37xPu9GY8") # latest version
sdata = artifact.load() # cache, then load into memory
group = artifact.open() # stream without downloading
path = artifact.cache() # sync into the local cache, returns the path Load from the CLI
# one-shot: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/jh4NRTq37xPu9GY8
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid jh4NRTq37xPu9GY8 Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["jh4NRTq37xPu9GY80000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart of collection
Perturb-map coupled with spatial transcriptomics identifies mutation associated gene signatures in a mouse model of lung adenocarcinoma→ 4 datasets · other
Metadata
- Technology
- Visium
- Organism
- mouse
- Tissue
- lung
- Condition
- lung adenocarcinoma
- Modality
- RNA
- Observations
- 2,546
- Tier
- bronze
- Chemistry
- Visium v1
- Magnification
- other
- Resolution
- 2,000 × 1,998 px
- mpp
- 3.916 µm/px
- Size
- 28 MB
- Sample ID
- GSM5808057
- License
- unknown
- Published
- Jan 15, 2022
Cite this dataset
No linked publication found — please cite the data source: NCBI GEO ↗.
Source: NCBI GEO ↗ · Reprocessed to SpatialData by SpatialData-DB